🚀 Day 59 at @ChaiCodeHQ ☕💻 This class was taught by @surajtwt_ Sir, where we explored how AI-powered applications work behind the scenes and how modern systems combine retrieval, automation, databases, and intelligent workflows to create smarter developer experiences. 📚 Topics Covered: 🧠 What is LLM → Understanding the foundation of modern AI systems 🔍 RAG Pipeline → Learning how retrieval improves AI responses 📌 Vector Embedding → Exploring how information is represented and processed 🎯 Query Embedding → Understanding smarter search and matching ⚡ Indexing → Learning techniques for faster retrieval 🗄️ Neon → Exploring modern database infrastructure 🔗 GitHub App Connection → Integrating applications with GitHub workflows ⚙️ Inngest Workflows → Automating tasks through event-driven execution A valuable session that gave me a better understanding of how modern AI applications are designed and built. Excited to continue learning and applying these concepts in future projects 🚀 Special thanks to @Hiteshdotcom Sir and @piyushgarg_dev Sir for creating such an amazing learning environment through @ChaiCodeHQ ☕💻 #ChaiAurCode #AI #LLM #RAG #VectorEmbedding #GitHub #Neon #Inngest #CodeReview #FullStackDeveloper #LearningInPublic #BuildInPublic

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Vector databases are the "long-term memory" of the AI era. By shifting from keyword matching to semantic meaning, they allow agents to recall context, learn preferences, and ground every action in your actual data. #AIAgents #VectorDatabases #VectorEmbedding

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Turn any #PDF into a chat assistant. Here’s a simple blueprint for building a PDF Chat system with RAG 👇 From OCR → embeddings → vector DB → LLM in one flow. #Python #Programming #RAG #GenAI #AI #VectorEmbedding #Markdown

(3/3) - Why embeddings matter 🚀 They power: ✔ Search ✔ Recommendations ✔ Chatbots ✔ Semantic understanding In short: 👉 Vector embeddings turn meaning into math so machines can think in context. #Python #VectorEmbedding #RAG #AI

(2/3) - These numbers place words in a math space. Similar meanings end up close together, and different meanings are far apart. So: • “Happy” ≈ “Joyful” • “Happy” ≠ “Angry” Distance = meaning. #Python #VectorEmbedding #RAG #AI

Vector embeddings are how machines turn meaning into math. It’s a way to turn words, sentences or images into numbers so computers can understand meaning. Instead of just text, the AI sees a list of values like: 👉 [0.42, -0.18, 0.77, -0.63] #Python #VectorEmbedding #RAG #AI

My mind was blown when I learned you can find semantic similarities between 2 words/sentences with math 😂 As in, you can mathematically tell “boy” is more similar to “male” than it is to “bus”. How do you even discover this? 😭 #ai #vectorembedding #vectordatabase

Famous words in tech world these days, RAG? and Langchain? Here in simple and short words #RohiNegi #CoderArmy #RAG #VectorEmbedding #Langchain #LLM #techworld